Dataset Information

The Kynurenine Pathway in Healthy Subjects and Subjects with Obesity, Depression and Chronic Obstructive Pulmonary Disease.

ABSTRACT:

Background

Changes in tryptophan metabolism through the kynurenine pathway (KP) are observed in several disorders and coupled with pathophysiological deviations.

Methods

This study retrospectively compared the KP in serum in healthy subjects (108) with subjects with obesity (141), depression (49), and chronic obstructive pulmonary disease (COPD) (22) participating in four clinical studies and explored predictors of the changes in the KP metabolites.

Results

Compared with the healthy group, the KP was upregulated in the disease groups with high kynurenine, quinolinic acid (QA), kynurenine/tryptophan-ratio and QA/xanthurenic acid-ratio and low kynurenic acid/QA-ratio. Tryptophan and xanthurenic acid were upregulated in the depressed group compared with the groups with obesity and COPD. The covariates BMI, smoking, diabetes, and C-reactive protein explained the significant differences between the healthy group and the group with obesity but not between the healthy group and the groups with depression and COPD, indicating that different pathophysiological conditions result in the same changes in the KP.

Conclusions

The KP was significantly upregulated in the disease groups compared with the healthy group, and there were significant differences between the disease groups. Different pathophysiological abnormalities seemed to result in the same deviations in the KP.

SUBMITTER: Farup PG

PROVIDER: S-EPMC10053928 | biostudies-literature | 2023 Feb

REPOSITORIES: biostudies-literature

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Publications

The Kynurenine Pathway in Healthy Subjects and Subjects with Obesity, Depression and Chronic Obstructive Pulmonary Disease.

Farup Per G PG Hamarsland Håvard H Mølmen Knut Sindre KS Ellefsen Stian S Hestad Knut K

Pharmaceuticals (Basel, Switzerland) 20230225 3

<h4>Background</h4>Changes in tryptophan metabolism through the kynurenine pathway (KP) are observed in several disorders and coupled with pathophysiological deviations.<h4>Methods</h4>This study retrospectively compared the KP in serum in healthy subjects (108) with subjects with obesity (141), depression (49), and chronic obstructive pulmonary disease (COPD) (22) participating in four clinical studies and explored predictors of the changes in the KP metabolites.<h4>Results</h4>Compared with th ...[more]

PMID: 36986451

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Project description:BackgroundAirflow limitation is a critical physiological feature in chronic obstructive pulmonary disease (COPD), for which long-term exposure to noxious substances, including tobacco smoke, is an established risk. However, not all long-term smokers develop COPD, meaning that other risk factors exist.ObjectiveThis study aimed to predict the risk factors for COPD diagnosis using machine learning in an annual medical check-up database.MethodsIn this retrospective observational cohort study (ARTDECO [Analysis of Risk Factors to Detect COPD]), annual medical check-up records for all Hitachi Ltd employees in Japan collected from April 1998 to March 2019 were analyzed. Employees who provided informed consent via an opt-out model were screened and those aged 30 to 75 years without a prior diagnosis of COPD/asthma or a history of cancer were included. The database included clinical measurements (eg, pulmonary function tests) and questionnaire responses. To predict the risk factors for COPD diagnosis within a 3-year period, the Gradient Boosting Decision Tree machine learning (XGBoost) method was applied as a primary approach, with logistic regression as a secondary method. A diagnosis of COPD was made when the ratio of the prebronchodilator forced expiratory volume in 1 second (FEV1) to prebronchodilator forced vital capacity (FVC) was <0.7 during two consecutive examinations.ResultsOf the 26,101 individuals screened, 1213 met the exclusion criteria, and thus, 24,815 individuals were included in the analysis. The top 10 predictors for COPD diagnosis were FEV1/FVC, smoking status, allergic symptoms, cough, pack years, hemoglobin A1c, serum albumin, mean corpuscular volume, percent predicted vital capacity, and percent predicted value of FEV1. The areas under the receiver operating characteristic curves of the XGBoost model and the logistic regression model were 0.956 and 0.943, respectively.ConclusionsUsing a machine learning model in this longitudinal database, we identified a number of parameters as risk factors other than smoking exposure or lung function to support general practitioners and occupational health physicians to predict the development of COPD. Further research to confirm our results is warranted, as our analysis involved a database used only in Japan.

Dataset Information

The Kynurenine Pathway in Healthy Subjects and Subjects with Obesity, Depression and Chronic Obstructive Pulmonary Disease.

Background

Methods

Results

Conclusions

Publications

The Kynurenine Pathway in Healthy Subjects and Subjects with Obesity, Depression and Chronic Obstructive Pulmonary Disease.

Similar Datasets

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